Abnormal Breast Detection Via Combination of Particle Swarm Optimization and Biogeography-Based Optimization

DOI

10.2991/icmeit-17.2017.69How to use a DOI?

Keywords

particle swarm optimization; biogeography-based optimization; abnormal breast; identification; classification; detection.

Abstract

The breast cancer is the most common cancer among women. To detect it in an accurate way, we designed a new abnormal breast detection system based on the hybridization of particle swarm optimization and biogeography-based optimization. The simulation results showed the sensitivity achieved 87.90ñ0.88%, the specificity achieved 87.20 ñ2.74%, and the accuracy achieved 87.55 ñ1.34%. Our method is better than two state-of-the-art methods.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Fangyuan Liu
AU  - Koji Nakamura
AU  - Rodney Payne
PY  - 2017/05
DA  - 2017/05
TI  - Abnormal Breast Detection Via Combination of Particle Swarm Optimization and Biogeography-Based Optimization
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 356
EP  - 360
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.69
DO  - 10.2991/icmeit-17.2017.69
ID  - Liu2017/05
ER  -